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elwood-0.1.2
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مشاهده بیشترتوضیحات
An open source dataset transformation, standardization, and normalization python library.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | elwood-0.1.2 |
| نام | elwood |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Brandon Rose, Powell Fendley |
| ایمیل نویسنده | info@jataware.com |
| آدرس صفحه اصلی | https://github.com/jataware/elwood |
| آدرس اینترنتی | https://pypi.org/project/elwood/ |
| مجوز | MIT license |
# Elwood
An open source dataset transformation, standardization, and normalization python library.
# Usage
To use start using Elwood, simply run:
`pip install elwood`
Now you are able to use any of the dataset transformation, standardization, or normalization functions exposed through this library. To start, simply include `from elwood import elwood` in your python file.
## Standardization
`elwood.process(args)`
#TODO STUB
## Transformation
The transformation functions include geographical extent clipping (latitude/longitude), geographical regridding (gridded data such as NetCDF or GeoTIFF), temporal clipping, and temporal scaling.
### Geospatial Clipping
`elwood.clip_geo(dataframe, geo_columns, polygons_list)`
This function takes a pandas dataframe, a geo_columns list of the column names for latitude and longitude, ex: `["lat", "lng"]`, and a list containing lists of objects representing the polygons to clip the data to. ex:
```
[
[
{
"lat": 11.0,
"lng": 42.0
},
{
"lat": 11.0,
"lng": 43.0
},
{
"lat": 12.0,
"lng": 43.0
},
{
"lat": 12.0,
"lng": 42.0
}
],
...
]
```
### Geospatial regridding
`elwood.regrid_dataframe_geo(dataframe, geo_columns, scale_multi)`
This function takes a dataframe and regrids it's geography by some scale multiplier that is provided. This multiplier will be used to divide the current geographical scale in order to make a more coarse grained resolution dataset. The dataframe must have a detectable geographical scale, meaning each lat/lon represents a point in the middle of a gridded cell for the data provided. Lat and lon and determined by the geo_columns passed in: a list of the column names ex: `["lat", "lng"]`
### Temporal Clipping
`elwood.clip_dataframe_time(dataframe, time_column, time_ranges)`
This function will produce a dataframe that only includes rows with `time_column` values contained within `time_ranges`. The time_ranges argument is a list of objects containing a start and end time. ex: `[{"start": datetime, "end": datetime}, ...]`
### Temporal Scaling
`elwood.rescale_dataframe_time(dataframe, time_column, time_bucket, aggregation_function_list)`
This function will produce a dataframe who's rows are the aggregated data based on some time bucket and some aggregation function list provided. The `time_column` is the name of the column containing targeted time values for rescaling. The `time_bucket` is some DateOffset, Timedelta or str representing the desired time granularity, ex. `'M', 'A', '2H'`. The `aggregation_function_list` is a list of aggregation functions to apply to the data. ex. `['sum']` or `['sum', 'min', 'max']`
## 0 to 1 Normalization
`elwood.normalize_features(dataframe, output_file)`
This function expects a dataframe with a "feature" column and a "value" column, or long data. Each entry for a feature has its own feature/value row.
This function returns a dataframe in which all numerical values under the "value" column for each "feature" have been 0 to 1 scaled.
Optionally you may specify an `output_file` name to generate a parquet file of the dataframe.
# Historys
نیازمندی
| مقدار | نام |
|---|---|
| ==1.0.1 | bump2version |
| ==8.0 | Click |
| ==4.5.4 | coverage |
| ==0.29.23 | Cython |
| ==3.7.8 | flake8 |
| >=0.18.0 | fuzzywuzzy |
| ==3.1.4 | GDAL |
| >=0.3.0 | geofeather |
| ==0.8.1 | geopandas |
| ==1.5.3 | netCDF4 |
| ==1.22 | numpy |
| ==3.0.7 | openpyxl |
| >=21.1 | pip |
| >=1.8.2 | pydantic |
| ==2.6.1.post1 | pyproj |
| >=0.12.2 | python-Levenshtein |
| ==0.8.3 | Rtree |
| - | shapely |
| ==1.8.5 | Sphinx |
| ==3.14.0 | tox |
| <5.0.0,>=4.41.1 | tqdm |
| ==1.14.0 | twine |
| ==0.9.0 | watchdog |
| ==0.33.6 | wheel |
| ==0.16.1 | xarray |
| ==2.0.1 | xlrd |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl elwood-0.1.2:
pip install elwood-0.1.2.whl
نصب پکیج tar.gz elwood-0.1.2:
pip install elwood-0.1.2.tar.gz